A Radial Basis Function Approach to Financial Time Series Analysis

Abstract

Nonlinear multivariate statistical techniques on fast computers offer the potential to capture more of the dynamics of the high dimensional, noisy systems underlying financial markets than traditional models, while making fewer restrictive assumptions. This thesis presents a collection of practical techniques to address important estimation and confidence issues for Radial Basis Function networks arising from such a data driven approach, including efficient methods for parameter estimation and pruning, a pointwise prediction error estimator, and a methodology for controlling the 'data mining' problem. Novel applications in the finance area are described, including customized, adaptive option pricing and stock price prediction. Radial basis functions, Option pricing, Parameter estimation, Time series prediction, Confidence, Stock market.

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Document Details

Document Type
Technical Report
Publication Date
Dec 01, 1993
Accession Number
ADA276408

Entities

People

  • James M. Hutchinson

Organizations

  • Massachusetts Institute of Technology

Tags

Communities of Interest

  • Energy and Power Technologies
  • Ground and Sea Platforms
  • Materials and Manufacturing Processes

DTIC Thesaurus Topics

  • Accuracy
  • Algorithms
  • Artificial Intelligence
  • Computational Science
  • Data Mining
  • Data Science
  • Databases
  • Estimators
  • Information Processing
  • Information Science
  • Knowledge Management
  • Network Science
  • Normal Distribution
  • Probability Distributions
  • Random Variables
  • Statistical Algorithms
  • Time Series Analysis

Readers

  • Distributed Systems and Data Platform Development
  • Economics
  • Statistical inference.

Technology Areas

  • AI & ML
  • AI & ML - Bayesian Inference
  • AI & ML - Machine Learning Algorithms
  • AI & ML - Neural Networks